Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv AI

Jun 19

ROSE: Benchmarking the Perception-to-Action Gap in Multimodal Models

arXiv:2606. 19965v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly expected to act on visual information, yet the same scene may require different actions under different task contexts.

By Yihao Wang, Zijian He, Jie Ren, Keze Wang

llms multimodal benchmarks

arXiv AI

Jun 19

Multi-Agent Transactive Memory

arXiv:2606. 19911v1 Announce Type: new Abstract: The decentralized deployment of LLM agents with diverse capabilities across diverse tasks motivates infrastructure for knowledge sharing across heterogeneous agent populations.

By To Eun Kim, Xuhong He, Dishank Jain, Ambuj Agrawal, Negar Arabzadeh, Fernando Diaz

llms rag agents

arXiv AI

Jun 19

Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models

arXiv:2606. 19750v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a central approach for improving reasoning capabilities in large language models (LLMs), where training efficiency depends critically on how problems are sampled during optimization.

By Darrien McKenzie, Nicklas Hansen, Xiaolong Wang

llms reinforcement-learning

arXiv Machine Learning

Jun 19

Probe-and-Refine Tuning of Repository Guidance for Coding Agents

arXiv:2606. 20512v1 Announce Type: cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to run the test suite, which workflows have historically led to wrong fixes) that does not exist in the code itself.

By Asa Shepard, Jeannie Albrecht

llms rag agents

arXiv AI

Jun 19

DeXposure-Claw: An Agentic System for DeFi Risk Supervision

arXiv:2606. 19501v1 Announce Type: new Abstract: Decentralized finance exposes supervisors to fast-moving, networked credit risks.

By Aijie Shu, Bowei Chen, Wenbin Wu, Cathy Yi-Hsuan Chen, Fengxiang He

llms agents

arXiv AI

Jun 19

GLARE: A Natural Language Interface for Querying Global Explanations

arXiv:2606. 19735v1 Announce Type: new Abstract: While global explanations are crucial for understanding vision models across datasets, classes, and decision contexts, their complex and monolithic nature often hinders practical exploration.

By Bhavan Vasu, Rajesh Mangannavar

llms data-engineering

arXiv AI

Jun 19

Interpretable and Verifiable Hardware Generation with LLM-Driven Stepwise Refinement

arXiv:2606. 19387v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable success in software development.

By You Li, Samuel Mandell, David Z. Pan

llms agents safety

arXiv AI

Jun 19

MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization

arXiv:2606. 20164v1 Announce Type: cross Abstract: Real-world clinical decision support requires reasoning over heterogeneous and longitudinal patient information rather than answering isolated medical questions.

By Aueaphum Aueawatthanaphisut

llms rag agents nlp multimodal

arXiv AI

Jun 19

The Scaffold Effect: How Prompt Framing Drives Apparent Multimodal Gains in Clinical VLM Evaluation

arXiv:2603. 28387v2 Announce Type: replace Abstract: Trustworthy clinical AI requires that performance gains reflect genuine evidence integration rather than surface-level artifacts.

By Doan Nam Long Vu, Simone Balloccu

llms multimodal safety

arXiv AI

Jun 19

How LLMs Fail and Generalize in RTL Coding for Hardware Design?

arXiv:2606. 19347v1 Announce Type: cross Abstract: Translating sequential programming priors into the parallel temporal logic of hardware design remains a crucial bottleneck for large language models(LLM).

By Guan-Ting Liu, Chao-Han Huck Yang, Chenhui Deng, Zhongzhi Yu, Brucek Khailany, Yu-Chiang Frank Wang

llms benchmarks safety

arXiv AI

Jun 19

PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models

arXiv:2606. 19534v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks.

By Yueyi Sun, Yuhao Wang, Jason Li, Ye Tian, Tao Zhang, Jacky Mai, Yihan Wang, Haochen Wang, Jinbin Bai, Ling Yang, Yunhai Tong

llms diffusion multimodal benchmarks

arXiv Machine Learning

Jun 19

A Unified Perspective on the Dynamics of Deep Transformers

arXiv:2501. 18322v2 Announce Type: replace Abstract: Transformers, which are state-of-the-art in most machine learning tasks, represent the data as sequences of vectors called tokens.

By Val\'erie Castin, Pierre Ablin, Jos\'e Antonio Carrillo, Gabriel Peyr\'e

llms benchmarks

arXiv AI

Jun 19

Analyzing the Narration Gap in LLM-Solver Loops

arXiv:2606. 19588v1 Announce Type: new Abstract: Formal tools such as SAT and SMT solvers are increasingly embedded in language model reasoning pipelines when a safety or security critical question can be formulated in logic.

By Zunchen Huang, Songgaojun Deng

llms safety

arXiv AI

Jun 19

Which Pairs to Compare for LLM Post-Training?

arXiv:2606. 19607v1 Announce Type: new Abstract: Preference-based post-training has become a central paradigm for aligning language models.

By Jiangze Han, Vineet Goyal, Will Ma

llms reinforcement-learning benchmarks

arXiv AI

Jun 19

Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer

arXiv:2606. 19346v1 Announce Type: cross Abstract: We study cross-lingual transfer by fine-tuning seven large language models (4B--671B parameters) on Arabic and evaluating zero-shot reading comprehension on Semitic languages and non-Semitic controls.

By Ahmed Haj Ahmed, Ruochen Zhang, Alvin Grissom II

llms fine-tuning benchmarks safety

arXiv AI

Jun 19

Diffusion Language Models: An Experimental Analysis

arXiv:2606. 19475v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized language modeling through autoregressive generation, enabling strong performance across a wide range of tasks.

By Thomas Bertolani, Davide Bucciarelli, Leonardo Zini, Marcella Cornia, Lorenzo Baraldi

llms diffusion benchmarks

arXiv AI

Jun 19

Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks

arXiv:2606. 19489v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) enhance interpretability by projecting learned features into a human-understandable concept space.

By Ya Wang, Adrian Paschke

llms rag multimodal benchmarks safety

arXiv AI

Jun 19

Variable-Length Tokenization via Learnable Global Merging for Diffusion Transformers

arXiv:2606. 20076v1 Announce Type: cross Abstract: Latent Diffusion Models (LDMs) have become dominant in visual synthesis, but their quality-compute trade-off is largely constrained by the tokenizer's fixed compression ratio.

By Dong Hoon Lee, Seunghoon Hong

llms diffusion nlp safety

arXiv Machine Learning

Jun 19

Online Dynamic Batching with Formal Guarantees for LLM Training

arXiv:2606. 19989v1 Announce Type: cross Abstract: Modern LLM training breaks a core assumption behind offline batch samplers: the true training cost of a sample is only observable after preprocessing, augmentation, templating, tokenization, and multimodal visual-token expansion.

By Dian Li, Zekun Wang, Yaoru Wang, Jiahong Yan

llms nlp fine-tuning efficiency multimodal safety

arXiv AI

Jun 19

AI Economist Agent: An Agentic Framework for Model-Grounded Economic Analysis with RAG, Knowledge Graphs, and Large Language Models

arXiv:2606. 20041v1 Announce Type: cross Abstract: We propose a model-grounded RAG-based AI economist with an agentic framework for economic scenario analysis using large language models (LLMs) and knowledge graphs.

By Masahiro Kato

llms rag agents

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